Advertisement

Machine Learning Techniques for Understanding Context and Process

  • Marko Grobelnik
  • Dunja Mladenić
  • Gregor Leban
  • Tadej Štajner
Chapter

Abstract

This chapter discusses how machine learning techniques can be useful for modelling and understanding context and processes. Machine learning techniques that have been applied for understanding context and processes are briefly presented together with the setting in which they have been applied. An example application focusing on context understanding is described to illustrate results of applying the techniques on real-world data. Interpretation and understanding of context in the ACTIVE knowledge workspace is described in  Chap. 5 and deployed at BT as described in  Chap. 9, while optimizing and sharing of knowledge processes is addressed in  Chap. 6.

Keywords

Machine Learning Technique Context Model Knowledge Worker Context Definition Context Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Banerjee A, Basu S, Merugu S (2007) Multi-way clustering on relation graphs. SDM SIAM, Minneapolis, MN April 26–28, pp 145–156Google Scholar
  2. Basu S, Davidson I, Wagstaff KL (2008) Constrained clustering: advances in algorithms, theory, and applications. Data mining and knowledge discovery series. Chapman and Hall/CRC, CRC Press, Florida, New YorkGoogle Scholar
  3. Davidson I, Ravi S (2005) Clustering with constraints: feasibility issues and the k-means algorithm. In: Proceedings of SDM, Newport Beach, CAGoogle Scholar
  4. Dong G, Pei J (2007) Sequence data mining. Springer-Verlag New York Inc., New YorkzbMATHGoogle Scholar
  5. Dubey A, Bhattacharya I, Godbole S (2010) A cluster-level semi-supervision model for interactive clustering. In: Proceedings of ECML PKDD 2010, Lecture Notes in AI, Springer-Verleg, Berlin/HeidelbergGoogle Scholar
  6. ECAI (2006) Workshop on context representation and reasoning. http://sra.itc.it/events/crr06, Accessed date 5 Aug 2011
  7. Grobelnik M, Mladenić D, Ferlež J (2009) Probabilistic temporal process model for knowledge processes: handling a stream of linked text. In: Proceedings of the 12th international conference information society – IS 2009, vol A. Institut Jožef Stefan, Ljubljana, pp 222–227Google Scholar
  8. Guo J, Sun C (2003) Context representation, transformation and comparison for ad hoc product data exchange. ACM DocEng 2003: proceedings of the 2003 ACM symposium on document engineering, ACM, Grenoble, France, New York, U.S., pp 121–130Google Scholar
  9. Jacquemont S, Jacquenet F, Sebban M (2009) Mining probabilistic automata: a statistical view of sequential pattern mining. Machine Learning 75(1):91–127CrossRefGoogle Scholar
  10. Leban G, Grobelnik M (2010) Displaying email-related contextual information using Contextify. In: Proceeding of ISWC 2010, LNCS, Springer, Heidelberg, pp 181–184Google Scholar
  11. Mitchell TM (1997) Machine learning. The McGraw-Hill Companies, Inc., New YorkzbMATHGoogle Scholar
  12. Mitchell TM (2006) The discipline of machine learning. CMU-ML-06-108 July 2006, School of Computer Science, Carnegie Mellon University, PittsburghGoogle Scholar
  13. Mitchell TM (2009) Mining our reality. Science 326(5960):1644–1645, http://www.sciencemag.org/content/326/5960/1644.short CrossRefGoogle Scholar
  14. Mladenić D (2006) Feature selection for dimensionality reduction. In: Subspace, latent structure and feature selection: statistical and optimization perspectives workshop, vol 3940, Lecture notes in computer science. Springer, Berlin, Heidelberg, Hershey, USA, pp 84–102CrossRefGoogle Scholar
  15. Mladenić D (2007) Text mining: machine learning on document. In: Encyclopedia of data warehousing and mining. Hershey [etc.], Idea Group Reference, cop., pp 1109–1112Google Scholar
  16. Štajner T, Mladenić D, Grobelnik M (2010) Exploring contexts and actions in knowledge processes. Workshop on context, information and ontologies, LisbonGoogle Scholar
  17. The Radicati Group Releases, Email statistics report, 2009–2013. http://www.radicati.com/
  18. VUT – Vienna University of Technology, Austria, 2007, D2.2 design and proof-of-concept implementation of the inContext context model version 1Google Scholar
  19. Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: Proceedings of ICML 2000, Morgan Kaufmann, MassachusettsGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marko Grobelnik
    • 1
  • Dunja Mladenić
    • 1
  • Gregor Leban
    • 1
  • Tadej Štajner
    • 1
  1. 1.Artificial Intelligence LaboratoryJozef Stefan InstituteLjubljanaSlovenia

Personalised recommendations